Advancing CO<sub>2</sub>RR with O-Coordinated Single-Atom Nanozymes: A DFT and Machine Learning Exploration
Hao Sun, Jing‐yao Liu
Abstract
Electrochemical CO 2 reduction reaction (CO 2 RR) offers a promising route toward zero-carbon emissions. Recently emerged single-atom nanozymes (SANs), which combine the advantages of single-atom catalysts (SACs) and nanozymes, show potential in CO 2 RR electrocatalysis applications. Herein, we designed 260 carbon-supported TMO 4 SANs, introducing heteroatoms outside the coordination sphere to modulate the interaction between the first-coordination sphere and the support structure. Using a collaborative workflow of the density functional theory (DFT) and machine learning (ML), we assessed these SANs for the CO 2 RR performance. Among them, 185 SANs demonstrated high stability. Our model, based on extreme gradient boosting regression (XGBR) and trained on 100 labeled SANs, successfully predicted the limiting potentials ( U L ) for 85 SANs. From these, we distinguished 33 SANs with superior activity, good specific product selectivity, and greater hydrogen evolution reaction (HER) suppression compared with benchmark TMN 4 -C catalysts. Particularly, MoO 4 supported on external H-doped graphene (Mo-I) and FeO 4 supported on external N,O-doped graphene (Fe-VI) exhibited remarkable enzyme-mimicking characteristics, with Mo-I showing a formate dehydrogenase-like behavior and Fe-VI mimicking CO dehydrogenase with U L values of −0.05 and −0.06 V, respectively. Through ML feature engineering and electronic structure property analysis, we determined the first-coordination sphere–support interaction (FCSSI) as a key descriptor in regulating the catalyst performance. We hope that these insights may provide valuable guidance for exploring potential SANs for CO 2 RR.